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Comparing the Performance of Fuzzy Operators in the Object-Based Comparing the Performance of Fuzzy Operators in the Object-based Image Analysis and Support Vector Machine Kernel Functions for the Snow Cover Estimation in Alvand Mountain Mostafa Karampour ( [email protected] ) Lorestan University https://orcid.org/0000-0002-5991-3803 Amirhossein Halabian University of Payam-e Noor Akbar Hosseini University of Lorestan Mostafa Mosapoor University of Payam-e Noor Research Article Keywords: Snow cover, hydrological processes, Sentinel-2B, fuzzy operators,SVM kernel functions, classication method. Posted Date: July 23rd, 2021 DOI: https://doi.org/10.21203/rs.3.rs-705609/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/15 Abstract Snow cover is a signicant driver in many ecological, climatic, and hydrological processes regarding the mountainous regions and high-latitude areas. Researchers believe that remote-sensing data can provide better estimates of snow cover ranges in comparison with the traditional surveying methods. Therefore, the present study was conducted using Sentinel-2B satellite images to compare the performance of the support vector machine (SVM) kernel functions and object-oriented fuzzy operators in estimating the amount of snow cover in the Alvand Mountain. In this research, the data consists of four Sentinel-2B satellite bands at 10 m spatial resolution (B2, 3, 4 & 8), launched on March 6, 2020. In this research study, the linear, polynomial, radial, and sigmoid SVM kernel functions, as well as the object-oriented fuzzy operators (AND, OR, MGE, MAR, MGWE, and ALP) have been employed. The results indicated that among these algorithms, AND algorithm, which represents the logical commonality, included the lowest return fuzzy value of 98%; therefore, this algorithm seems to provide the overall highest accuracy. Based on these ndings, in the digital image classication, the object-oriented processing method can make it possible to achieve the highest accuracy compared to the SVM kernel functions. The reason is that a wide range of information, such as texture, shape, position, content, and bandwidth is associated with the objects in this classication method. 1. Introduction Snow is an important water resource. In the mountainous regions, particularly the arid ones, snowmelt water is the primary source of many rivers. Moreover, the global radiant energy balance is highly affected by the albedo and low thermal conductivity of snow cover (Liu, C et al 2020). Also, in such high-latitude areas, the snow cover is a signicant driver in many ecological, climatic and hydrological processes (Gascoin, S et al 2019). Since snow can be considered as the water storage with a short delay during the seasonal run-off, it constitutes a crucial component of the hydrological cycle (Muhammad, S. and Thapa, A 2020). Thus, the seasonal snowpack has considerable control over the hydrology and economy in many mountainous and cold regions globally. Similarly, the snow variability affects various ecological procedures, such as the species composition, distribution, and phenology (Alonso-González, E et al 2018). Therefore, achieving a deeper understanding of the present and future climate, water cycle, and ecological changes requires an accurate assessment of the seasonal snow cover. The climatological, hydrological, and ecological signicance of snow cover is linked to its energy storage, high reectance, good insulating properties, remarkable thermal capacity, being a substantial water storage resource, with the eventual release during the melting season (Czyzowska-Wisniewski, E et al 2015). Monitoring the snow cover is an important means of studying its spatial and temporal changes, as well as the distribution analysis of the regional precipitation. This climatic phenomenon can be assessed using the measuring stations, modeling, remote sensing technology, and applied programs. Although accurate information regarding the measurement location can be provided through the ground stations, these stations are still limited in terms of the spatial scale; that is because providing sucient information to produce long-term data about snow (on a spatial scale) is not achievable in many parts of Page 2/15 the world when the information is obtained through a scattered network of meteorological stations. Nevertheless, the spatial and temporal characteristics of snow can still be monitored through modeling. Yet, the accuracy of such modeling results has been proven to be low due to the lack of information regarding the initial conditions (Azizi, G et al 2017). Moreover, due to the limited number of meteorological stations and the pointwise measurements thereof, these stations are not suitable alternatives for studying snow as a continuous phenomenon. Also, the snoweld measurement and sampling are timely procedures and not cost ecient. Alternatively, using remote sensing technology not only makes it possible to access high-altitude sites, but also it is generally less expensive than the formerly mentioned methods. Furthermore, satellites are proper imagery tools for measuring snow cover due to the snow reectance and the visible contrast between snowelds and most surfaces (Raispour, K 2016). Remote sensing data can provide better estimates of snow cover ranges compared to the traditional surveying methods. Thus, nowadays, the use of remote sensing data with more accurate information on snow cover is an operational method of ecient water resource management (Mirmousavi, S. H. and Saboor, L 2014). The image processing methods of remote sensing can be divided into two general categories. The rst category with a single-pixel processing unit is called the pixel-based method. The processing units in the second category include image objects or a group of pixels; in other words, since a homogeneous group of pixels or the object image is the main processing unit, the image is processed in the object space and not in the pixel one; this makes it possible to dene additional properties other than the spectral one, such as the shape, size, texture, and neighborhood (Momeni, M., Khosravi, I. and Mostaejeran, B 2013). There are many research studies worldwide on measuring the snow cover level and the trend of its changes using remote sensing. For instance, Lopez et al. (2008), after monitoring the images from the period of 2000–2006 based on the NDSI index, examined the amount of snow cover and its changes in northern Patagonia. The results of this study marked the minimum snow cover with an area of 3600 km² in March 2007 and the maximum snow cover with an area of 11323 km² in August 2001. Boi (2009) presented a snow cover monitoring technique for Italy and the Alpine regions using visible, near infrared and infrared MSG data. Accordingly, the monthly and annual maps regarding the snow cover frequency has been estimated (Boi, P 2010). In another study, Mölg et al. (2010) examined and controlled the snow cover data of MODIS multi-temporal imagery at high altitudes of Italy. In this study, snow cover estimation in time- series images from 2002 to 2008 was conducted; the output maps were derived from combining Aqua and Terra snow cover maps, thereby reducing the cloudless and value-free pixels. Additionally, the snow cover maps, obtained from Landsat E.T.M. + satellite images, were utilized to validate the results. Moreover, this study conrmed the classication improvement by a combination of Aqua and Terra images. Finally, using the object-oriented fuzzy classication and Landsat satellite data, Farhan et al. (2018) estimated the changes in the seasonal snow cover level in the Astore River Basin (western Himalayan part of Pakistan). Subsequent to the segmentation of the satellite images, the degree of fuzzy membership was determined, and the area’s snow cover level was estimated. As such, López-Moreno et al. (2020) considered the long-term trends of snow cover duration and depth from December to April of the years from 1958 to 2017 in the Pyrenees. The Mann–Kendall test illustrated that snow cover duration Page 3/15 and its average depth decreased during the research time scope; moreover, the persistent warming was proved to be a major factor for the snow cover decrease in the Pyrenees. At last, the present study was performed to compare the performance of the support vector machine (SVM) kernel functions and object-oriented fuzzy operators in estimating the snow cover amount in Alvand Mountain (Hamadan Province, Iran) using Sentinel-2B satellite images. 2. The Study Area Alvand Mountain is an individual mountain with an area of 1375 km², in the eastern branches of the central Zagros Mountains, located near the cities of Hamadan, Tuyserkan, Asadabad, and Bahar. It is connected to the Mounts Khodabandehlou and Chehel-Cheshmeh in the Kurdistan Province from the north-west and it stretches towards Rasvand altitudes and Mount Vafs in Arak from the south-east. The ridge of Alvand Mountain forms a natural boundary among Hamadan, Tuyserkan, and Alvand; moreover its highest peak with an altitude of 3584 meters is located 18 km to the south of Hamedan city. Geographically, this mountain divides the Hamadan Province into northern and southern parts while stretching from the north-west to south-east (Jafari, G. and Hosseini, S. A 2017). In Fig. 1 the location of the study area has been demonstrated. 3. Data And Method The data used in this research consists of four Sentinel-2B satellite bands at 10 m spatial resolution (B2, 3, 4 & 8) that were launched on March 6, 2020. These bands were downloaded from the Sentinel Online technical website (sentinel.esa.int). The radiometric corrections were performed using the Sen2Cor plugin in the SNAP software. Subsequently, the bands were saved as an information layer with the TIFF extension. ENVI image analysis software was used to classify the SVM kernel functions and the calculation of accuracy. The Trimble eCognition Suite was utilized for the segmentation and classication of object-oriented fuzzy operators and the calculation of the accuracy. Finally, the most accurate algorithm was converted into a shape le using the ArcMap software and the area of snow cover was obtained.
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